On low-power, low-cost Internet of Things (IoT) edges, coarse-grained entropy source-based physical-layer key generation (PKG) is often used, which results in a very low bit generation rate (BGR). In this paper, a novel PKG scheme, Hinge, designed to adapt to varying environmental conditions is introduced to optimize the trade-off between the bit mismatch rate (BMR) and BGR using fine-grained entropy sources on IoT devices. Hinge predicts channel reciprocity levels from one side and dynamically adjusts the quantization strategy, maintaining a low BMR while maximizing BGR. Compared with existing PKG solutions on Bluetooth devices, Hinge yields significant improvements in BGR, with a comparable BMR. Through extensive experiments, Hinge showcases its potential for providing a secure and efficient key generation mechanism for IoT devices in complex real-world scenarios.

Liquid sensing in ubiquitous contexts plays an essential role in various scenarios. Recently, some wireless sensing systems have been proposed for liquid identification. However, existing works usually require specific equipment or capture the signals penetrating a target, limiting the deployability of liquid sensing. In large-scale scenarios, multiple devices are usually required to expand the coverage area due to the RFID reader antenna's reading range limitation. To enlarge the sensing range and make the liquid sensing method can be adopted in real moving scenarios, in this paper, we presentMobileLiquidIDentification (MLiquID), a liquid sensing system that can recognize the type of liquid in a mobile manner with commercial off-the-shelf (COTS) RFID devices. This mobile process leads to continuous variation in location, so the major challenge in this paper is how to extract signal features from the superimposed information of movement and material. The key insight is to regard movement as an opportunity to acquire data from different perspectives instead of a challenge to hinder feature extraction. We construct a Phase-RSS model by analyzing the influence of moving and liquid on the phase and RSS signals. First, we propose a method to calculate the distance from the tag to the reader antenna. Second, we explore an identification method to identify liquid type by extracting signal features Phase-RSS coefficient$C_{P-R}$and Maximum Response Distance (MRD). Experimental results demonstrate an average accuracy of 96.80% in identifying 10 common liquids, which shows the great potential of MLiquID for mobile liquid sensing.

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications.

Bluetooth Low Energy (BLE) Mesh is a pivotal multi-hop self-organizing network in the Internet of Things (IoT) domain, offering low power consumption, low cost, and robustness. This paper presents a comprehensive study on the communication performance of BLE-Mesh using commercial off-the-shelf devices, focusing on the impact of key mesh parameters such as transmission power, packet interval, and network structure on performance. Through extensive indoor and outdoor experiments, we quantify the impact of these parameters and conduct a detailed study. Our findings provide insights into the actual communication range of BLE-Mesh, the effect of node design on overall network performance, and the configuration for optimal performance. The research contributes to the establishment of a BLE-Mesh network in real-world environments, answering critical questions for practitioners, and offering a reference for future BLE-Mesh deployments. This work furthers our understanding of the characteristics, challenges, and future directions of BLE-Mesh, setting the stage for advancements in IoT applications such as smart offices and homes.

The evolution of Wi-Fi to next-generation 802.11bf demonstrates the potential of device-free Wi-Fi sensing applications, where we can remotely infer the behaviors of users without bringing into physical contact with them. Among these sensing applications, Wi-Fi tracking is critical to provide location based services. Recent Wi-Fi tracking systems can be cataloged into model-based and data-based approaches: (1) the model-based approach is to build the mathematical tracking model. However, this method is sensitive to environmental noise, and spends more execution time; (2) the data-based approach is to train a neural network. However, this method requires a lot of efforts to collect training dataset, and cannot handle all types of trajectories well. To resolve these issues, we propose theNNE-Tracking, a Neural Network Enhanced tracking framework. The core design principle ofNNE-Trackingis as follows: we improve the tracking accuracy based on the data-based approach, and utilize the model-based approach to supervise whether the neural network is already working well. Moreover, we also design a framework to estimate unknown parameters of the tracking model, so that the system can automatically generate the Wi-Fi map. We take the Wi-Fi passive tracking as a specific example to explain how to applyNNE-Trackingin practical applications. Experimental results demonstrate that our design can reduce 59.4% ∼ 85.3% tracking errors while significantly saving execution time. As for deployment costs, we can automatically infer the Wi-Fi map without manual calibration; As for stability, when we repeat the training process with different hidden layers and random seeds, the tracking standard deviation of these neural networks is only 1.4cm.

Mobile robot-assisted book inventory such as book identification and book order detection has become increasingly popular in smart library, replacing the manual book inventory which is time-consuming and error-prone. The existing systems are either computer vision (CV)-based or RFID-based, however several limitations are inevitable. CV-based systems may not be able to identify books effectively due to low accuracy of detecting texts on book spine. RFID tags attached to books can be used to identify a book uniquely. However, in high tag density scenarios such as library, tag coupling effects of adjacent tags may seriously affect the accuracy of tag reading. To overcome these limitations, this paper presents a novel RFID and CV fusion system for Book Inventory using mobile robot (RC-BI). RFID and CV are first used individually to obtain book order, then the information will be fused by the sequence based matching algorithm to remove ambiguity and improve overall accuracy. Specifically, we address three technical challenges. We design a deep neural network (DNN) model with multiple inputs and mixed data to filter out interference of RFID tags on other tiers, and propose a video information extracting schema to extract book spine information accurately, and use strong link to align and match RFID- and CV-based timestamp vs. book-name sequences to avoid errors during fusion. Extensive experiments indicate that our system achieves an average accuracy of 98.4% for tier filtering and an average accuracy of 98.9% for book order, significantly outperforming the state-of-the-arts.

Localization of target tagged objects on the shelf is of great significance in RFID-enabled warehousing scenarios. Compared with the RFID localization systems that use fixed reader antennas or mobile RFID-robot, the portable reader-based methods are much more cost-effective. Hence, this paper focuses on reader-portable RFID localization. However, the existing reader-portable localization systems suffer from the following limitations: (i) reader antenna is required to pass by the target tags. Thus, the tags in the corner can never be located; (ii) many reference tags need to be deployed on the shelf in advance, which considerably increases the manpower; (iii) specialized antenna is required, which limits the promotion potential. To this end, this paper proposes a Waving action-driven RFID Localization (WRL) system, which enables tag localization with a portable camera-augmented reader. In the WRL system, a user only needs to wave the camera-augmented reader before locating the target tags. Specifically, we first use a classical camera pose estimation method named PnP to recover the antenna’s movement trajectory in a pixel coordinate system. Then, WRL constructs a gridded hologram, in which camera data and RFID phase data are jointly used to calculate a probability for each grid. Intuitively, the higher probability a grid has, the more possible the target tag lies in the corresponding grid. Based on this idea, WRL calculates the target tag’s location on the shelf. We use the Commercial-Off-The-Shelf (COTS) RFID and camera devices to implement the WRL system. Extensive experiments have been conducted, and the results demonstrate that the mean localization error of WRL is less than 20cm with a confidence of about 95%.

Individual activity recognition is crucial for Human-Computer Interaction (HCI) applications, especially in multi-person scenarios. Current approaches, based on wearable sensors or wireless signals (e.g., WiFi and RFID), however, are often focused on single person scenario only, due to the limitation of existing wireless sensing technologies. In order to address the issue, we design a DEeper Exercise Monitoring system, called DEEM, in which we introduce computer vision techniques to facilitate RFID devices to provide exercise estimation support, as well as identifying the users and the objects users hold. We implement this design with COTS Kinect camera and RFID devices in a smart gym application. To the best of our knowledge, it is the first system for estimating multiple people behavior in a complicated gym environment. We conduct extensive experiments to evaluate the performance of the DEEM system. The experimental results show that the matching accuracy can reach 95%, and the exercise estimation accuracy can reach 94% on average.

Under the influence of multipath effects and small scale fading, the robustness and reliability of the existing human detection methods based on radio frequency signals are easy to be impaired. In this paper, we propose a novel design that using the multi-layer filtering of channel state information (CSI) to identify moving targets in dynamic environments and analyze the gait periodicity of human. We employ an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose a profile matching mechanism for human detection and a periodicity analysis mechanism for human gait taking advantage of the above design. We evaluated it with the commodity Wi-Fi infrastructures in different environments. Experimental results indicate that our approach performs identification of human with an average accuracy of 94%.